All 24 Papers — Ainiketan

Group 1: The Foundations

1950–1997

Computing Machinery and Intelligence

The paper that started it all. Alan Turing asked a deceptively simple question — can machines think? — and in trying to answer it, invented the entire framework through which we still think about artificial intelligence today.

The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain

Frank Rosenblatt

Turing asked if machines could think. Rosenblatt built one that could learn. The Perceptron is the grandfather of every neural network alive today — the first machine that adjusted itself based on experience, rather than following rules someone wrote by hand.

Learning Representations by Back-propagating Errors

The Perceptron could learn, but only simple patterns. Multi-layer networks could learn complex patterns, but nobody knew how to train them. This paper answered that question — with a single elegant algorithm that is still the beating heart of every neural network trained today.

Long Short-Term Memory

Group 2: The Word Embedding Revolution

2013–2014

Efficient Estimation of Word Representations in Vector Space (Word2Vec)

Sequence to Sequence Learning with Neural Networks

Neural Machine Translation by Jointly Learning to Align and Translate

Group 3: The Transformer and Scale

2017–2020

Attention Is All You Need

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Improving Language Understanding by Generative Pre-Training

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Language Models are Few-Shot Learners

Scaling Laws for Neural Language Models

Group 4: Alignment and Reasoning

2022–2023

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Training Language Models to Follow Instructions with Human Feedback

Let's Verify Step by Step: A Process Supervision Approach to Reward Modeling

Group 5: Efficiency and Open Models

2023

LLaMA: Open and Efficient Foundation Language Models

Ring Attention with Blockwise Transformers for Near-Infinite Context

Group 6: Frontier Models

2023–2024

Gemini: A Family of Highly Capable Multimodal Models

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Constitutional AI: Harmlessness from AI Feedback

Group 7: Reasoning and Agents

2025

Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Model Parameters

rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

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